Evidence flow graph methods for validation and verification of expert systems

The results of an investigation into the use of evidence flow graph techniques for performing validation and verification of expert systems are given. A translator to convert horn-clause rule bases into evidence flow graphs, a simulation program, and methods of analysis were developed. These tools were then applied to a simple rule base which contained errors. It was found that the method was capable of identifying a variety of problems, for example that the order of presentation of input data or small changes in critical parameters could affect the output from a set of rules

Comprehensibility & Overfitting Avoidance in Genetic Programming for Technical Trading Rules

This paper presents two methods for increasing comprehensibility in technical trading rules produced by Genetic Programming. For this application domain adding a complexity penalizing factor to the objective fitness function also avoids overfitting the training data. Using pre-computed derived technical indicators, although it biases the search, can express complexity while retaining comprehensibility. Several of the learned technical trading rules outperform a buy and hold strategy for the S&P500 on the testing period from 1990-2002, even taking into account transaction costs

Renormalization of the periodic Anderson model: an alternative analytical approach to heavy Fermion behavior

In this paper a recently developed projector-based renormalization method (PRM) for many-particle Hamiltonians is applied to the periodic Anderson model (PAM) with the aim to describe heavy Fermion behavior. In this method high-energetic excitation operators instead of high energetic states are eliminated. We arrive at an effective Hamiltonian for a quasi-free system which consists of two non-interacting heavy-quasiparticle bands. The resulting renormalization equations for the parameters of the Hamiltonian are valid for large as well as small degeneracy $\nu_f$ of the angular momentum. An expansion in $1/\nu_f$ is avoided. Within an additional approximation which adapts the idea of a fixed renormalized \textit{f} level $\tilde{\epsilon}_{f}$, we obtain coupled equations for $\tilde{\epsilon}_{f}$ and the averaged \textit{f} occupation $$. These equations resemble to a certain extent those of the usual slave boson mean-field (SB) treatment. In particular, for large $\nu_f$ the results for the PRM and the SB approach agree perfectly whereas considerable differences are found for small $\nu_f$.Comment: 26 pages, 5 figures included, discussion of the DOS added in v2, accepted for publication in Phys. Rev.

Not all the bots are created equal:the Ordering Turing Test for the labelling of bots in MMORPGs

This article contributes to the research on bots in Social Media. It takes as its starting point an emerging perspective which proposes that we should abandon the investigation of the Turing Test and the functional aspects of bots in favor of studying the authentic and cooperative relationship between humans and bots. Contrary to this view, this article argues that Turing Tests are one of the ways in which authentic relationships between humans and bots take place. To understand this, this article introduces the concept of Ordering Turing Tests: these are sort of Turing Tests proposed by social actors for purposes of achieving social order when bots produce deviant behavior. An Ordering Turing Test is method for labeling deviance, whereby social actors can use this test to tell apart rule-abiding humans and rule-breaking bots. Using examples from Massively Multiplayer Online Role-Playing Games, this article illustrates how Ordering Turing Tests are proposed and justified by players and service providers. Data for the research comes from scientific literature on Machine Learning proposed for the identification of bots and from game forums and other player produced paratexts from the case study of the game Runescape

Geophysical Methods: an Overview

Geophysics is expected to have a major role in lunar resource assessment when manned systems return to the Moon. Geophysical measurements made from a lunar rover will contribute to a number of key studies: estimating regolith thickness, detection of possible large-diameter lava tubes within maria basalts, detection of possible subsurface ice in polar regions, detection of conductive minerals that formed directly from a melt (orthomagmatic sulfides of Cu, Ni, Co), and mapping lunar geology beneath the regolith. The techniques that can be used are dictated both by objectives and by our abilities to adapt current technology to lunar conditions. Instrument size, weight, power requirements, and freedom from orientation errors are factors we have considered. Among the geophysical methods we believe to be appropriate for a lunar resource assessment are magnetics, including gradiometry, time-domain magnetic induction, ground-penetrating radar, seismic reflection, and gravimetry

Distributed preservation services: integrating planning and actions

Digital preservation has turned into an active field of research. The most prominent approaches today are migration and emulation; especially considering migration, a range of working tools is available, each with specific strengths and weaknesses. The decision process on which actions to take to preserve a given set of digital ob jects for future access, i.e., preservation planning, is usually an ad-hoc procedure with little tool support and even less support for automation. This paper presents the integration of tools and services for object migration and characterization through a service oriented architecture into a planning tool called Plato, thus creating a distributed and highly automated preservation planning environment.Fundação para a Ciência e a Tecnologia (FCT) - bolsa SFRH/BD/17334/2004.Digital Preservation Europ

Training on a Lower Body Positive Pressure Treadmill With Body Weight Support does not Improve Aerobic Capacity

International Journal of Exercise Science 14(7): 829-839, 2021. This study examined the physiological changes resulting from training on a lower body positive pressure treadmill (LBPPT) at three different levels of body weight support (BWS). Thirty-three healthy college aged students (22.3 ± 3.1 years) completed the study. Participants performed a graded exercise test (GXT) to exhaustion and were placed into one of three experimental groups corresponding to 100%, 75%, and 50% of their normal BW. Participants trained at their experimental BW levels for eight-weeks. Training speed was monitored by heart rate (HR) and speed was adjusted to elicit approximately 60% of participant’s peak oxygen uptake (V̇O2peak) at normal BW prior to including body weight support (BWS). One-way analysis of variance (ANOVA) was used to compare the change in aerobic capacity. The 100% BW group improved their relative V̇O2peak (1.42 ± 1.52 ml · min-1 · kg-1) when compared to the 50% BW group (-0.87 ± 2.20 ml · min-1 · kg-1 [p = .022]) but not the 75% BW group (-0.16 ± 1.92 ml · min-1 · kg-1, [p = .14]). Furthermore, no statistical differences in V̇O2peak were observed between the 75% and 50% BW groups (p = .66). Based on this study, training at 75% and 50% of normal BW on a LBPPT does not improve aerobic capacity compared to training with no BWS when using training speeds derived from a GXT with full BW. The outcome of this study may help to prescribe training speeds while utilizing a LBPPT to maintain or improve aerobic capacity

D-SPACE4Cloud: A Design Tool for Big Data Applications

The last years have seen a steep rise in data generation worldwide, with the development and widespread adoption of several software projects targeting the Big Data paradigm. Many companies currently engage in Big Data analytics as part of their core business activities, nonetheless there are no tools and techniques to support the design of the underlying hardware configuration backing such systems. In particular, the focus in this report is set on Cloud deployed clusters, which represent a cost-effective alternative to on premises installations. We propose a novel tool implementing a battery of optimization and prediction techniques integrated so as to efficiently assess several alternative resource configurations, in order to determine the minimum cost cluster deployment satisfying QoS constraints. Further, the experimental campaign conducted on real systems shows the validity and relevance of the proposed method